The human visual system adapts to changing illuminant sources by ensuring that a white object appears white. When a white object travels from daylight, which has more blue color component, to incandescent light, which has more red color component, the human visual system makes adjustment to balance the red, green, and blue color components to ensure that a white object appears white in both daylight and incandescent light. The technique of balancing the red color, green color, and blue color components is known as white balance. Thus, the human visual system automatically white balances an image to preserve the true white color of a white object in the image as the object travels under different illuminant types. Image capture systems use automatic white balance algorithms to mimic the human visual mechanism in order to reproduce the true white color of a white object in an image under different illuminant sources.
The strength of the RGB color components varies significantly in different light conditions. There is far more blue color component in daylight than in interior cool white fluorescent (CWF) light. Table 1 provides a color temperature index for different illuminant types. Higher color temperature, such as daylight, has more blue color component while lower color temperature, such as incandescent light, has more red color component.
TABLE 1Color Temperature IndexIlluminant TypeColor TemperatureDaylight5000-7500 K   Cool White Fluorescent4500 KU30 (General Office Light)3000 KA (Incandescent Light)2000 K
Prior art automatic white balance methods assume that the whole image needs to be white balanced. This assumption causes the over inclusion of RGB values of all pixels of an image in calculating the average RGB values. The average RGB values are used to adjust color gains in a captured image. In other words, the amount of color gain to apply to each color channel is based on making the red, green, and blue color components equal to the average RGB values. When RGB values of all pixels are included in calculating the average RGB values, undesirable influence from strong colors will also be incorporated. When a strong color object enters or leaves a scene, its influence will skew the average RGB values. A strong color contribution in the average RGB calculation can ultimately cause an object to lose its true color. For example, when a red object enters a scene with a red background, this image will have a predominant red color value. The red color will heavily influence the average RGB values of this image. The red color contribution in the average RGB values is so strong that the affect on the gain adjustment can cause an object to lose its true color.
Another prior art method of automatic white balancing defines a single white area in a color space diagram for all illuminant types in an attempt to combat strong color influences. This method uses a color space diagram to identify the white pixels of an image. The white area in a color space diagram serves as a template for detecting the white pixels of an image. If a pixel has a value falling within the white area then it is determined to be a white pixel and its RGB values will be used to calculate the average RGB values for color gain adjustments.
A drawback of using a color space diagram with a single white area is the possibility of incorrectly including non-white pixels in calculating the average RGB value. In some instance, a strong color pixel has similar attribute as a white pixel and can fall within the white area of a color space diagram. The non-white pixels can have a negative effect on the RGB averaging calculation. For example, strong blue pixels have similar characteristics as white pixels in daylight. When an image contains strong blue pixels, they will incorrectly be construed as white pixels and their RGB values will be included in the RGB averaging calculation. The contribution from the strong blue pixels will result in incorrect average RGB values, which will be used for determining gain adjustments.
Additionally, this method cannot be used to ascertain the illuminant source of the image because a single white area for all illuminant type does not have sufficient information to support further analysis to obtain the identity of the illuminant source.
Thus, there is a need for a robust automatic white balance mechanism that can eliminate strong color influences and has the capability to respond quickly to changes in illuminant source.